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Indonesian Journal of Medical Chemistry and Bioinformatics

Abstract

Lung cancer is the leading cause of cancer death worldwide. About 2.1 million lung cancer patients were diagnosed in 2018, accounting for about 11.6% of all newly diagnosed cancer cases. For lung cancer, blood is the first choice as a source of screening biomarker candidates. Blood biomarkers provide a snapshot of the patient's entire body, including the primary tumor, metastatic disease, immune response, and peritumoral stroma. However, sputum sampling, bronchial lavage or aspiration, exhaled breath (EB), and airway epithelial sampling represent unique samples for lung cancer and other airway cancers as potential sources for alternative biomarkers. Metabolites are products of cell metabolism that are unique biomarkers in a disease. In this article, we aim to find metabolite biomarkers using machine learning. Metabolite data were obtained from Metabolomic workbench, while detection and identification were performed in silico. From 82 samples, controls and cancers, we found 158 metabolites and analyzed them. From the analysis, we found 3 metabolites that play an important role in lung cancer and found 1 metabolite that is the most influential. From there we found that glutamic acid is one of the best biomarker candidates we provide for detecting lung cancer. However, this simulation still needs to be improved in order to find other biomarkers that can provide a better detection of lung cancer

Bahasa Abstract

Kanker paru-paru merupakan penyebab utama kematian akibat kanker di seluruh dunia. Pada tahun 2018, sekitar 2,1 juta pasien kanker paru-paru didiagnosis, menyumbang sekitar 11,6% dari semua kasus kanker yang baru didiagnosis. Untuk kanker paru-paru, darah menjadi pilihan pertama sebagai sumber kandidat biomarker untuk skrining. Biomarker darah memberikan gambaran keseluruhan tubuh pasien, termasuk tumor utama, penyakit metastatik, respons kekebalan, dan stroma peritumoral. Namun, pengambilan sampel sputum, bronchial lavage atau aspirasi, exhale breath (EB), dan pengambilan sampel epitel saluran udara menjadi sampel unik untuk kanker paru-paru dan kanker saluran udara lainnya sebagai sumber potensial biomarker alternatif. Metabolit adalah produk metabolisme sel yang menjadi biomarker unik dalam suatu penyakit. Dalam artikel ini, kami bertujuan untuk menemukan biomarker metabolit menggunakan machine learning. Data metabolit diperoleh dari Metabolomic Workbench, sedangkan deteksi dan identifikasi dilakukan secara in silico. Dari 82 sampel, kontrol dan kanker, kami menemukan 158 metabolit dan menganalisanya. Dari analisis tersebut, kami menemukan 3 metabolit yang memainkan peran penting dalam kanker paru-paru dan menemukan 1 metabolit yang paling berpengaruh. Dari situ, kami menemukan bahwa asam glutamat adalah salah satu kandidat biomarker terbaik yang kami sediakan untuk mendeteksi kanker paru-paru. Namun, simulasi ini masih perlu diperbaiki agar dapat menemukan biomarker lain yang dapat memberikan deteksi kanker paru-paru yang lebih baik.

References

  1. Chen, Y.; Ma, Z.; Li, A.; Li, H.; Wang, B.; Zhong, J.; et. al. Metabolomic profiling of human serum in lung cancer patients using liquid chromatography/hybrid quadrupole time-of-flight mass spectrometry and gas chromatography/mass spectrometry. J Cancer Res Clin Oncol, 2015, 141(4):705–18.
  2. Noreldeen, H.A.A.; Liu, X.; Xu, G. Metabolomics of lung cancer: Analytical platforms and their applications. J Sep Sci, 2020, 43(1):120–33.
  3. Sun, Q.; Zhao, W.; Wang, L.; Guo, F.; Song, D.; Zhang, Q.; et. al. Integration of metabolomic and transcriptomic profiles to identify biomarkers in serum of lung cancer. J Cell Biochem, 2019, 120(7):11981–9.
  4. Chen, W.; Lu, S.; Ou, J.; Wang, G.; Zu, Y.; Chen, F.; et. al. Metabonomic characteristics and biomarker research of human lung cancer tissues by HR1H NMR spectroscopy. Cancer Biomarkers, 2016, 16(4):653–64.
  5. Callejón-Leblic, B.; García-Barrera, T.; Pereira-Vega, A.; Gómez-Ariza, J.L. Metabolomic study of serum, urine and bronchoalveolar lavage fluid based on gas chromatography mass spectrometry to delve into the pathology of lung cancer. J Pharm Biomed Anal, 2019, 163:122–9.
  6. Callejón-Leblic, B.; García-Barrera, T.; Grávalos-Guzmán, J.; Pereira-Vega, A.; Gómez-Ariza, J.L. Metabolic profiling of potential lung cancer biomarkers using bronchoalveolar lavage fluid and the integrated direct infusion/ gas chromatography mass spectrometry platform. J Proteomics, 2016, 145:197–206.
  7. Lubes, G.; Goodarzi, M. GC–MS based metabolomics used for the identification of cancer volatile organic compounds as biomarkers. J Pharm Biomed Anal, 2018, 147:313–22.
  8. Bamji-Stocke, S.; van Berkel, V.; Miller, D.M.; Frieboes, H.B. A review of metabolism-associated biomarkers in lung cancer diagnosis and treatment. Metabolomics, 2018, 14(6):0.
  9. Camacho, D.M.; Collins, K.M.; Powers, R.K.; Costello, J.C.; Collins, J.J. Next-generation machine learning for biological networks. Cell, 2018, 173(7): 1581-1592.
  10. Saccenti, E.; Hoefsloot, H.C.; Smilde, A.K.; Westerhuis, J.A.; Hendriks, M.M. Reflections on univariate and multivariate analysis of metabolomics data. Metabolomics, 2014, 10(3); 361-374.
  11. Johnson, C.H.; Ivanisevic, J.; Siuzdak, G. Metabolomics: beyond biomarkers and towards mechanisms. Nature Reviews Molecular Cell Biology, 2016, 17(7): 451-459.
  12. Tan, Y.; Hanson, J.A.; Chu, J.; Yang, H. VIP MSM UBER WIP PCA ESSENTIAL Protein Dynamics [Internet]. Proteins, 2014, 1084: 51–62. Available from: http://link.springer.com/10.1007/978-1-62703-658-0
  13. Salem, K.B.; Abdelaziz, A.B. Analyse de composantes principales (ACP). Journal of The Tunisian Society of Medical Sciences, 2021, 99(04): 383–9.
  14. Jollife, I.T.; Cadima, J. Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2016, 374(2065).
  15. Jolliffe, I.T.; Cadima, J.Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2016, 374(2065), 20150202.
  16. Ledesma, R.D.; Valero-Mora, P.; Macbeth, G. The scree test and the number of factors: a dynamic graphics approach. Span J Psychol, 2015, 18:E11.
  17. Octaviani, P.A.; Wilandari, Y.; Ispriyanti, D. Penerapan metode SVM pada data akreditasi sekolah dasar di kabupaten magelang. Jurnal Gaussian, 2014, 3(8):811–20.
  18. Setyati, W.A.; Sunaryo, S.; Rezagama, A.; Widodo, A.K.; Yulianto, M.F.A. Penerapan regresi logistik dalam penentuan faktor yang mempengaruhi jumlah wisatawan ecotourism desa bedono. Jurnal Enggano, 2020, 5(1):11–22.
  19. Dananjaya, R.H.; Sutrisno, S.; Fitriady, S. Penerapan Artificial Neural Network (Ann) dalam memprediksi kapasitas dukung fondasi tiang. Matriks Teknik Sipil, 2022, 10(4):419.
  20. Ratnawati, L.; Sulistyaningrum, D.R. Penerapan random forest untuk mengukur tingkat keparahan penyakit pada daun apel. Jurnal Sains dan Seni ITS, 2020, 8(2).
  21. Syahfitra, F.D.; Syahputra, R.; Putra, K.T. Implementation of backpropagation artificial neural network as a forecasting system of power transformer peak load at bumiayu substation. Journal of Electrical Technology UMY, 2017, 1(3):118–25.
  22. Akbar, M.K.; Rahman, A.A. Metabolomic insights into tuberculosis : machine learning approaches for biomarker identification. Indonesian Journal of Medical Chemistry and Bioinformatics, 2023, 2,
  23. Yang, L.; Venneti, S.; Nagrath, D. Glutaminolysis: a hallmark of cancer metabolism. Annual Review of Biomedical Engineering, 2016, 18, 163-194.

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